Overview

Dataset statistics

Number of variables16
Number of observations731
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.6 KiB
Average record size in memory187.2 B

Variable types

Numeric10
Categorical6

Warnings

dteday has a high cardinality: 731 distinct values High cardinality
temp is highly correlated with atempHigh correlation
atemp is highly correlated with tempHigh correlation
registered is highly correlated with cntHigh correlation
cnt is highly correlated with registeredHigh correlation
instant is uniformly distributed Uniform
dteday is uniformly distributed Uniform
instant has unique values Unique
dteday has unique values Unique
weekday has 105 (14.4%) zeros Zeros

Reproduction

Analysis started2021-03-04 06:34:39.421328
Analysis finished2021-03-04 06:34:58.034163
Duration18.61 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

instant
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366
Minimum1
Maximum731
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:34:58.126927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.5
Q1183.5
median366
Q3548.5
95-th percentile694.5
Maximum731
Range730
Interquartile range (IQR)365

Descriptive statistics

Standard deviation211.1658116
Coefficient of variation (CV)0.5769557695
Kurtosis-1.2
Mean366
Median Absolute Deviation (MAD)183
Skewness0
Sum267546
Variance44591
MonotocityStrictly increasing
2021-03-03T22:34:58.261553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7311
 
0.1%
2511
 
0.1%
2491
 
0.1%
2481
 
0.1%
2471
 
0.1%
2461
 
0.1%
2451
 
0.1%
2441
 
0.1%
2431
 
0.1%
2421
 
0.1%
Other values (721)721
98.6%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
7311
0.1%
7301
0.1%
7291
0.1%
7281
0.1%
7271
0.1%
7261
0.1%
7251
0.1%
7241
0.1%
7231
0.1%
7221
0.1%

dteday
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct731
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
2012-01-21
 
1
2011-06-20
 
1
2012-07-25
 
1
2012-06-11
 
1
2012-08-27
 
1
Other values (726)
726 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique731 ?
Unique (%)100.0%

Sample

1st row2011-01-01
2nd row2011-01-02
3rd row2011-01-03
4th row2011-01-04
5th row2011-01-05
ValueCountFrequency (%)
2012-01-211
 
0.1%
2011-06-201
 
0.1%
2012-07-251
 
0.1%
2012-06-111
 
0.1%
2012-08-271
 
0.1%
2012-07-141
 
0.1%
2012-07-241
 
0.1%
2011-09-291
 
0.1%
2012-04-231
 
0.1%
2012-08-181
 
0.1%
Other values (721)721
98.6%
2021-03-03T22:34:58.570722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-01-211
 
0.1%
2011-06-201
 
0.1%
2012-07-251
 
0.1%
2012-06-111
 
0.1%
2012-08-271
 
0.1%
2012-07-141
 
0.1%
2012-07-241
 
0.1%
2011-09-291
 
0.1%
2012-04-231
 
0.1%
2012-08-181
 
0.1%
Other values (721)721
98.6%

Most occurring characters

ValueCountFrequency (%)
11728
23.6%
01626
22.2%
21527
20.9%
-1462
20.0%
3170
 
2.3%
5134
 
1.8%
7134
 
1.8%
8134
 
1.8%
4132
 
1.8%
6132
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5848
80.0%
Dash Punctuation1462
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
11728
29.5%
01626
27.8%
21527
26.1%
3170
 
2.9%
5134
 
2.3%
7134
 
2.3%
8134
 
2.3%
4132
 
2.3%
6132
 
2.3%
9131
 
2.2%
ValueCountFrequency (%)
-1462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7310
100.0%

Most frequent character per script

ValueCountFrequency (%)
11728
23.6%
01626
22.2%
21527
20.9%
-1462
20.0%
3170
 
2.3%
5134
 
1.8%
7134
 
1.8%
8134
 
1.8%
4132
 
1.8%
6132
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7310
100.0%

Most frequent character per block

ValueCountFrequency (%)
11728
23.6%
01626
22.2%
21527
20.9%
-1462
20.0%
3170
 
2.3%
5134
 
1.8%
7134
 
1.8%
8134
 
1.8%
4132
 
1.8%
6132
 
1.8%

season
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
3
188 
2
184 
1
181 
4
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%
2021-03-03T22:34:58.804097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:34:58.883876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring characters

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ValueCountFrequency (%)
3188
25.7%
2184
25.2%
1181
24.8%
4178
24.4%

yr
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
1
366 
0
365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
1366
50.1%
0365
49.9%
2021-03-03T22:34:59.116313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:34:59.192113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1366
50.1%
0365
49.9%

Most occurring characters

ValueCountFrequency (%)
1366
50.1%
0365
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

ValueCountFrequency (%)
1366
50.1%
0365
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

ValueCountFrequency (%)
1366
50.1%
0365
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ValueCountFrequency (%)
1366
50.1%
0365
49.9%

mnth
Real number (ℝ≥0)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.519835841
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:34:59.269894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.451912787
Coefficient of variation (CV)0.5294478069
Kurtosis-1.20911201
Mean6.519835841
Median Absolute Deviation (MAD)3
Skewness-0.008148650127
Sum4766
Variance11.91570189
MonotocityNot monotonic
2021-03-03T22:34:59.378604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1262
8.5%
1062
8.5%
862
8.5%
762
8.5%
562
8.5%
362
8.5%
162
8.5%
1160
8.2%
960
8.2%
660
8.2%
Other values (2)117
16.0%
ValueCountFrequency (%)
162
8.5%
257
7.8%
362
8.5%
460
8.2%
562
8.5%
660
8.2%
762
8.5%
862
8.5%
960
8.2%
1062
8.5%
ValueCountFrequency (%)
1262
8.5%
1160
8.2%
1062
8.5%
960
8.2%
862
8.5%
762
8.5%
660
8.2%
562
8.5%
460
8.2%
362
8.5%

holiday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
0
710 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%
2021-03-03T22:34:59.635915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:34:59.716702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ValueCountFrequency (%)
0710
97.1%
121
 
2.9%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997264022
Minimum0
Maximum6
Zeros105
Zeros (%)14.4%
Memory size5.8 KiB
2021-03-03T22:34:59.794492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.004786918
Coefficient of variation (CV)0.6688723127
Kurtosis-1.254282352
Mean2.997264022
Median Absolute Deviation (MAD)2
Skewness0.002741597663
Sum2191
Variance4.019170586
MonotocityNot monotonic
2021-03-03T22:34:59.887245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6105
14.4%
1105
14.4%
0105
14.4%
5104
14.2%
4104
14.2%
3104
14.2%
2104
14.2%
ValueCountFrequency (%)
0105
14.4%
1105
14.4%
2104
14.2%
3104
14.2%
4104
14.2%
5104
14.2%
6105
14.4%
ValueCountFrequency (%)
6105
14.4%
5104
14.2%
4104
14.2%
3104
14.2%
2104
14.2%
1105
14.4%
0105
14.4%

workingday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
1
500 
0
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1500
68.4%
0231
31.6%
2021-03-03T22:35:00.130593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:35:00.211377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring characters

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
68.4%
0231
31.6%

weathersit
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
1
463 
2
247 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters731
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%
2021-03-03T22:35:00.418516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:35:00.499301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number731
100.0%

Most frequent character per category

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common731
100.0%

Most frequent character per script

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII731
100.0%

Most frequent character per block

ValueCountFrequency (%)
1463
63.3%
2247
33.8%
321
 
2.9%

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct499
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4953847885
Minimum0.0591304
Maximum0.861667
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:00.613041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0591304
5-th percentile0.2135685
Q10.3370835
median0.498333
Q30.6554165
95-th percentile0.76875
Maximum0.861667
Range0.8025366
Interquartile range (IQR)0.318333

Descriptive statistics

Standard deviation0.1830509961
Coefficient of variation (CV)0.3695127512
Kurtosis-1.118864155
Mean0.4953847885
Median Absolute Deviation (MAD)0.158333
Skewness-0.05452096476
Sum362.1262804
Variance0.03350766718
MonotocityNot monotonic
2021-03-03T22:35:00.755660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2658335
 
0.7%
0.6355
 
0.7%
0.43754
 
0.5%
0.5641674
 
0.5%
0.6491674
 
0.5%
0.4841674
 
0.5%
0.684
 
0.5%
0.6966674
 
0.5%
0.7108334
 
0.5%
0.5141673
 
0.4%
Other values (489)690
94.4%
ValueCountFrequency (%)
0.05913041
0.1%
0.09652171
0.1%
0.09739131
0.1%
0.10751
0.1%
0.12751
0.1%
0.1347831
0.1%
0.1383331
0.1%
0.1443481
0.1%
0.151
0.1%
0.1508331
0.1%
ValueCountFrequency (%)
0.8616671
0.1%
0.8491671
0.1%
0.8483331
0.1%
0.8383331
0.1%
0.8341671
0.1%
0.831
0.1%
0.8283331
0.1%
0.82751
0.1%
0.82251
0.1%
0.8183331
0.1%

atemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct690
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4743539886
Minimum0.0790696
Maximum0.840896
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:00.914235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0790696
5-th percentile0.2206455
Q10.3378425
median0.486733
Q30.608602
95-th percentile0.714967
Maximum0.840896
Range0.7618264
Interquartile range (IQR)0.2707595

Descriptive statistics

Standard deviation0.1629611784
Coefficient of variation (CV)0.3435433922
Kurtosis-0.9851305305
Mean0.4743539886
Median Absolute Deviation (MAD)0.135624
Skewness-0.1310880421
Sum346.7527657
Variance0.02655634566
MonotocityNot monotonic
2021-03-03T22:35:01.054860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6546884
 
0.5%
0.6370083
 
0.4%
0.3756213
 
0.4%
0.5429292
 
0.3%
0.6035542
 
0.3%
0.5378962
 
0.3%
0.2430582
 
0.3%
0.3516292
 
0.3%
0.5947042
 
0.3%
0.4501212
 
0.3%
Other values (680)707
96.7%
ValueCountFrequency (%)
0.07906961
0.1%
0.09883911
0.1%
0.1016581
0.1%
0.1161751
0.1%
0.117931
0.1%
0.1193371
0.1%
0.1262751
0.1%
0.1442831
0.1%
0.1495481
0.1%
0.1508831
0.1%
ValueCountFrequency (%)
0.8408961
0.1%
0.8263711
0.1%
0.8049131
0.1%
0.8042871
0.1%
0.7948291
0.1%
0.7903961
0.1%
0.7866131
0.1%
0.7859671
0.1%
0.7613671
0.1%
0.7575791
0.1%

hum
Real number (ℝ≥0)

Distinct595
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6278940629
Minimum0
Maximum0.9725
Zeros1
Zeros (%)0.1%
Memory size5.8 KiB
2021-03-03T22:35:01.215329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4074545
Q10.52
median0.626667
Q30.7302085
95-th percentile0.8685415
Maximum0.9725
Range0.9725
Interquartile range (IQR)0.2102085

Descriptive statistics

Standard deviation0.1424290951
Coefficient of variation (CV)0.2268361871
Kurtosis-0.06453013469
Mean0.6278940629
Median Absolute Deviation (MAD)0.104584
Skewness-0.06978343399
Sum458.99056
Variance0.02028604714
MonotocityNot monotonic
2021-03-03T22:35:01.359946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6133334
 
0.5%
0.5683333
 
0.4%
0.54253
 
0.4%
0.7529173
 
0.4%
0.6970833
 
0.4%
0.6308333
 
0.4%
0.6053
 
0.4%
0.593
 
0.4%
0.693
 
0.4%
0.7295833
 
0.4%
Other values (585)700
95.8%
ValueCountFrequency (%)
01
0.1%
0.1879171
0.1%
0.2541671
0.1%
0.2758331
0.1%
0.291
0.1%
0.3021741
0.1%
0.3051
0.1%
0.311251
0.1%
0.3141671
0.1%
0.3143481
0.1%
ValueCountFrequency (%)
0.97251
0.1%
0.9704171
0.1%
0.96251
0.1%
0.9495831
0.1%
0.9482611
0.1%
0.9395651
0.1%
0.931
0.1%
0.9291671
0.1%
0.9251
0.1%
0.92251
0.1%

windspeed
Real number (ℝ≥0)

Distinct650
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1904862116
Minimum0.0223917
Maximum0.507463
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:01.505553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0223917
5-th percentile0.07961665
Q10.13495
median0.180975
Q30.2332145
95-th percentile0.343283
Maximum0.507463
Range0.4850713
Interquartile range (IQR)0.0982645

Descriptive statistics

Standard deviation0.07749787068
Coefficient of variation (CV)0.4068424167
Kurtosis0.4109222677
Mean0.1904862116
Median Absolute Deviation (MAD)0.049129
Skewness0.6773454211
Sum139.2454207
Variance0.00600591996
MonotocityNot monotonic
2021-03-03T22:35:01.650126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2288583
 
0.4%
0.1187923
 
0.4%
0.1349543
 
0.4%
0.1498833
 
0.4%
0.1666673
 
0.4%
0.11073
 
0.4%
0.1368173
 
0.4%
0.1679123
 
0.4%
0.106353
 
0.4%
0.1169082
 
0.3%
Other values (640)702
96.0%
ValueCountFrequency (%)
0.02239171
0.1%
0.04230421
0.1%
0.04540421
0.1%
0.04540831
0.1%
0.046651
0.1%
0.0472751
0.1%
0.05037921
0.1%
0.05287081
0.1%
0.0532131
0.1%
0.0572251
0.1%
ValueCountFrequency (%)
0.5074631
0.1%
0.4415631
0.1%
0.4222751
0.1%
0.4216421
0.1%
0.4179081
0.1%
0.4154291
0.1%
0.41481
0.1%
0.4092121
0.1%
0.4073461
0.1%
0.3980081
0.1%

casual
Real number (ℝ≥0)

Distinct606
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.1764706
Minimum2
Maximum3410
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:01.792526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile88
Q1315.5
median713
Q31096
95-th percentile2355
Maximum3410
Range3408
Interquartile range (IQR)780.5

Descriptive statistics

Standard deviation686.6224883
Coefficient of variation (CV)0.8095278661
Kurtosis1.322074327
Mean848.1764706
Median Absolute Deviation (MAD)396
Skewness1.266454032
Sum620017
Variance471450.4414
MonotocityNot monotonic
2021-03-03T22:35:01.927127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9684
 
0.5%
1204
 
0.5%
2443
 
0.4%
6533
 
0.4%
6393
 
0.4%
1233
 
0.4%
1403
 
0.4%
1633
 
0.4%
7753
 
0.4%
6922
 
0.3%
Other values (596)700
95.8%
ValueCountFrequency (%)
21
0.1%
92
0.3%
151
0.1%
251
0.1%
341
0.1%
382
0.3%
411
0.1%
421
0.1%
431
0.1%
461
0.1%
ValueCountFrequency (%)
34101
0.1%
32831
0.1%
32521
0.1%
31601
0.1%
31551
0.1%
30651
0.1%
30311
0.1%
29631
0.1%
28551
0.1%
28461
0.1%

registered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct679
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3656.172367
Minimum20
Maximum6946
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:02.088695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile1177.5
Q12497
median3662
Q34776.5
95-th percentile6280.5
Maximum6946
Range6926
Interquartile range (IQR)2279.5

Descriptive statistics

Standard deviation1560.256377
Coefficient of variation (CV)0.426745848
Kurtosis-0.7130971386
Mean3656.172367
Median Absolute Deviation (MAD)1155
Skewness0.04365877989
Sum2672662
Variance2434399.962
MonotocityNot monotonic
2021-03-03T22:35:02.232353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48413
 
0.4%
17073
 
0.4%
62483
 
0.4%
35782
 
0.3%
44292
 
0.3%
17302
 
0.3%
38482
 
0.3%
21152
 
0.3%
38402
 
0.3%
6742
 
0.3%
Other values (669)708
96.9%
ValueCountFrequency (%)
201
0.1%
4161
0.1%
4321
0.1%
4511
0.1%
4721
0.1%
4911
0.1%
5701
0.1%
5731
0.1%
5771
0.1%
6541
0.1%
ValueCountFrequency (%)
69461
0.1%
69171
0.1%
69111
0.1%
68981
0.1%
68441
0.1%
68201
0.1%
68031
0.1%
67901
0.1%
67811
0.1%
67501
0.1%

cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct696
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4504.348837
Minimum22
Maximum8714
Zeros0
Zeros (%)0.0%
Memory size5.8 KiB
2021-03-03T22:35:02.392921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile1331
Q13152
median4548
Q35956
95-th percentile7576
Maximum8714
Range8692
Interquartile range (IQR)2804

Descriptive statistics

Standard deviation1937.211452
Coefficient of variation (CV)0.4300758049
Kurtosis-0.8119223847
Mean4504.348837
Median Absolute Deviation (MAD)1407
Skewness-0.04735278012
Sum3292679
Variance3752788.208
MonotocityNot monotonic
2021-03-03T22:35:02.535542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51192
 
0.3%
44012
 
0.3%
19772
 
0.3%
68242
 
0.3%
51912
 
0.3%
10962
 
0.3%
52022
 
0.3%
58472
 
0.3%
53122
 
0.3%
47582
 
0.3%
Other values (686)711
97.3%
ValueCountFrequency (%)
221
0.1%
4311
0.1%
4411
0.1%
5061
0.1%
6051
0.1%
6231
0.1%
6271
0.1%
6831
0.1%
7051
0.1%
7541
0.1%
ValueCountFrequency (%)
87141
0.1%
85551
0.1%
83951
0.1%
83621
0.1%
82941
0.1%
82271
0.1%
81731
0.1%
81671
0.1%
81561
0.1%
81201
0.1%

Interactions

2021-03-03T22:34:43.815728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:43.965360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:44.137866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:44.292503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:44.462998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:44.621605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:44.871903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.070507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.332478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.526377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.675940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.824541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:45.981163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.118359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.252983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.388641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.529216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.670837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.794532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:46.940169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.072789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.219414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.384928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.543598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.679187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:47.840782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.009304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.143997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.329962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.502499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.665107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.830185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:48.980781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.153294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.315886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.481419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.657947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.830509image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:49.977091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.125720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.300228image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.447859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.596436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.751023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:50.901654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.037339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.175980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.310646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.439315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.585922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.720565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.852164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:51.996803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.146500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.269173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.406858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.534517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.674094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.817757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:52.956385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.085045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.222675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.364249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.498926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.646533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.781299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:53.922920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.076543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.240121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.377759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.521370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.670970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.802620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:54.954271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.090899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.235463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.394083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.541693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.699224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:55.877746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.077212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.230803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.405335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.535047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.674779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.809469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:56.945098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:57.066769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:57.186451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:34:57.319090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-03-03T22:35:03.711607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-03T22:35:03.969916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-03T22:35:04.226233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-03T22:35:04.490613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-03T22:35:04.729942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-03T22:34:57.559459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-03T22:34:57.899551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
012011-01-0110106020.3441670.3636250.8058330.160446331654985
122011-01-0210100020.3634780.3537390.6960870.248539131670801
232011-01-0310101110.1963640.1894050.4372730.24830912012291349
342011-01-0410102110.2000000.2121220.5904350.16029610814541562
452011-01-0510103110.2269570.2292700.4369570.1869008215181600
562011-01-0610104110.2043480.2332090.5182610.0895658815181606
672011-01-0710105120.1965220.2088390.4986960.16872614813621510
782011-01-0810106020.1650000.1622540.5358330.26680468891959
892011-01-0910100010.1383330.1161750.4341670.36195054768822
9102011-01-1010101110.1508330.1508880.4829170.2232674112801321

Last rows

instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
7217222012-12-22111206010.2658330.2361130.4412500.40734620515441749
7227232012-12-23111200010.2458330.2594710.5154170.13308340813791787
7237242012-12-24111201120.2313040.2589000.7913040.077230174746920
7247252012-12-25111212020.2913040.2944650.7347830.1687264405731013
7257262012-12-26111203130.2433330.2203330.8233330.3165469432441
7267272012-12-27111204120.2541670.2266420.6529170.35013324718672114
7277282012-12-28111205120.2533330.2550460.5900000.15547164424513095
7287292012-12-29111206020.2533330.2424000.7529170.12438315911821341
7297302012-12-30111200010.2558330.2317000.4833330.35075436414321796
7307312012-12-31111201120.2158330.2234870.5775000.15484643922902729